Frontiers in oncology · 2026
Integrating intratumoral and peritumoral radiomics with deep transfer learning from multiparametric MRI for preoperative prediction of HER2 status in breast cancer: a multicenter study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Saisai, Rong Jing, Liu Tiantian, Yin Xiujuan, Xue Shuqin, Yin Likang, Liu Lei, Ji Yang, Gong Xijun, Wang Xiao
Аннотация
PURPOSE: To develop and validate a combined model integrating intratumoral and peritumoral radiomics features, deep transfer learning features from multiparametric MRI (DCE-MRI, T2WI, and DWI), and clinical indicators, and to evaluate its diagnostic performance and clinical utility for preoperative prediction of HER2 expression status in breast cancer.
METHODS: We collected data from 411 breast cancer patients from three centers retrospectively (training set: 212; internal validation set: 91; external test sets: 50 and 58). Multiparametric MRI (DCE/T2WI/DWI) was acquired. This study extracted manually constructed radiomics features and deep transfer-learning features based on ResNet50 from the tumor interior and peritumoral regions using multiparametric MRI. Through multiple feature-selection steps, such as intraclass correlation coefficient calculation, Spearman correlation testing, and LASSO logistic regression, a fusion feature set of deep learning and radiomics (DLR) was constructed. Finally, the DLR feature set was combined with independent clinical predictors to establish a combined prediction model. The model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis, and visual interpretability analysis was conducted using Grad-CAM and SHAP methods.
RESULTS: The combined model had the best accuracy and prediction ability, with AUC values of 0.965 (95% CI: 0.939-0.990) and 0.904 (95% CI: 0.843-0.966) for the training and internal validation cohorts, respectively. In external test sets 1 and 2, it had AUCs of 0.844 (95% CI: 0.724-0.964) and 0.846 (95% CI: 0.743-0.949), respectively.
CONCLUSIONS: By integrating intratumoral/peritumoral features from multiparametric MRI, radiomics, and deep transfer learning, and combining these with clinical indicators, the developed model enables precise prediction of HER2 status in breast cancer. This provides a reliable assessment tool for precision diagnosis and treatment decisions.
METHODS: We collected data from 411 breast cancer patients from three centers retrospectively (training set: 212; internal validation set: 91; external test sets: 50 and 58). Multiparametric MRI (DCE/T2WI/DWI) was acquired. This study extracted manually constructed radiomics features and deep transfer-learning features based on ResNet50 from the tumor interior and peritumoral regions using multiparametric MRI. Through multiple feature-selection steps, such as intraclass correlation coefficient calculation, Spearman correlation testing, and LASSO logistic regression, a fusion feature set of deep learning and radiomics (DLR) was constructed. Finally, the DLR feature set was combined with independent clinical predictors to establish a combined prediction model. The model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis, and visual interpretability analysis was conducted using Grad-CAM and SHAP methods.
RESULTS: The combined model had the best accuracy and prediction ability, with AUC values of 0.965 (95% CI: 0.939-0.990) and 0.904 (95% CI: 0.843-0.966) for the training and internal validation cohorts, respectively. In external test sets 1 and 2, it had AUCs of 0.844 (95% CI: 0.724-0.964) and 0.846 (95% CI: 0.743-0.949), respectively.
CONCLUSIONS: By integrating intratumoral/peritumoral features from multiparametric MRI, radiomics, and deep transfer learning, and combining these with clinical indicators, the developed model enables precise prediction of HER2 status in breast cancer. This provides a reliable assessment tool for precision diagnosis and treatment decisions.